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Towards Goals in Informed Agent Negotiation

  • Paul Bogg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

Abstract

Negotiation is typically the way in which real world multi-issue commercial contracts are resolved and signed. The automation of negotiation in the B2B context has the potential to revolutionise trade. Intelligent agents have been proposed as the software architecture for automating negotiation. Goals are important based on the notion from [1] that an agent which focuses on negotiating by interests or goals rather than positions may increase the quality of agreement, and the speed of reaching the agreement. This paper presents work towards goal based negotiation that extends an information theoretical framework for automating negotiation using maximum entropy inference with linear information constraints. We place goals in terms of the information inference model, with a view towards goal based argumentation.

Keywords

Intelligent Agent Automate Negotiation Trading Agent Maximum Entropy Distribution Attribute Goal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul Bogg
    • 1
  1. 1.University of Technology SydneyBroadwayAustralia

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